Survey synthesizes learning-augmented algorithms with formal guarantees

This paper surveys learning-augmented algorithms, which leverage fallible predictions while maintaining formal performance guarantees. It synthesizes various prediction interfaces, error measures, and construction mechanisms across different problem domains. The survey also distinguishes between theoretical upper bounds and empirical system-level evidence, highlighting open problems in cost-aware prediction and benchmarking. AI

IMPACT Provides a structured overview of methods for integrating machine learning predictions into algorithms while maintaining formal guarantees, potentially guiding future research and development.

RANK_REASON The item is a survey paper on a machine learning topic. [lever_c_demoted from research: ic=1 ai=1.0]

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